class_docstr: The Ray Serve integration engine needs to have a working connection to Ray Serve. For this:
      - A Ray Serve server should be running
  
  Example:
      
code: def create(
      self,
      target: str,
      df: Optional[pd.DataFrame] = None,
      args: Optional[Dict] = None,
  ) -> None:
      # TODO: use join_learn_process to notify users when ray has finished the training process
      args = args["using"]  # ignore the rest of the problem definition
      args["target"] = target
      self.model_storage.json_set("args", args)
      try:
          resp = requests.post(
              args["train_url"],
              json={
                  "df": df.to_json(orient="records"),
                  "target": target,
              },
              headers={
                  "content-type": "application/json; format=pandas-records"
              },
          )
      except requests.exceptions.InvalidSchema:
          raise Exception(
              "Error: The URL provided for the training endpoint is invalid."
          )
  
      resp = resp.json()
      if resp["status"] != "ok":
          raise Exception("Error: Training failed: " + resp["status"])
  
file_path: mindsdb/mindsdb/integrations/handlers/ray_serve_handler/ray_serve_handler.py
imports: import requests
  from typing import Dict, Optional
  import pandas
  from mindsdb.integrations.libs.base import BaseMLEngine
label_desc: Use image node_id 2 for calling the RayServeHandler obj's underlying member method code with example usage: obj.create(target, df, args) without return types